Unveiling the Molecular Mechanism of Trastuzumab Resistance in SKBR3 and BT474 Cell Lines for HER2 Positive Breast Cancer.

HER2-positive breast cancer is one of the most prevalent forms of cancer among women worldwide. Generally, the molecular characteristics of this breast cancer include activation of human epidermal growth factor receptor-2 (HER2) and hormone receptor activation. HER2-positive is associated with a higher death rate, which led to the development of a monoclonal antibody called trastuzumab, specifically targeting HER2. The success rate of HER2-positive breast cancer treatment has been increased; however, drug resistance remains a challenge. This fact motivated us to explore the underlying molecular mechanisms of trastuzumab resistance. For this purpose, a two-fold approach was taken by considering well-known breast cancer cell lines SKBR3 and BT474. In the first fold, trastuzumab treatment doses were optimized separately for both cell lines. This was done based on the proliferation rate of cells in response to a wide variety of medication dosages. Thereafter, each cell line was cultivated with a steady dosage of herceptin for several months. During this period, six time points were selected for further in vitro analysis, ranging from the untreated cell line at the beginning to a fully resistant cell line at the end of the experiment. In the second fold, nucleic acids were extracted for further high throughput-based microarray experiments of gene and microRNA expression. Such expression data were further analyzed in order to infer the molecular mechanisms involved in the underlying development of trastuzumab resistance. In the list of differentially expressed genes and miRNAs, multiple genes (e.g., BIRC5, E2F1, TFRC, and USP1) and miRNAs (e.g., hsa miR 574 3p, hsa miR 4530, and hsa miR 197 3p) responsible for trastuzumab resistance were found. Downstream analysis showed that TFRC, E2F1, and USP1 were also targeted by hsa-miR-8485. Moreover, it indicated that miR-4701-5p was highly expressed as compared to TFRC in the SKBR3 cell line. These results unveil key genes and miRNAs as molecular regulators for trastuzumab resistance.

N-Confused strapped calix[4]pyrrole: the missing member of calix[4]pyrrole chemistry.

The first example of N-confused strapped calix[4]pyrrole 5 is presented. The structural integrity of 5 and its regular isomer 4 was unambiguously confirmed by single crystal X-ray diffraction analysis. Anion binding studies using 1H NMR titration carried out in CDCl3 revealed a small but detectable tendency of 5 to interact with an anion. Conversely, the isomeric regular strapped calix[4]pyrrole 4 displayed high selectivity for fluoride anions under similar experimental conditions. The high fluoride selectivity of 4 and unexpectedly low anion affinity of 5 were ascribed to the presence of intramolecular hydrogen bonds within strapping subunits.

Carbazole Integrated Tetrakis-(1 H-pyrrole-2-carbaldehyde): A Highly Selective Fluorescent Probe for HP2O73.

A new carbazole-coupled tetrakis-(1 H-pyrrole-2-carbaldehyde) anion receptor 1 has been designed and synthesized. Anion binding studies in organic media using fluorescence and UV-vis spectroscopy revealed that receptor 1 is capable of sensing HP2O73- with high selectivity. Addition of HP2O73- to THF solution of 1 resulted in the emergence of a new broad band at longer wavelength along with quenching of the original emission band forming a ratiometric response. Based on dynamic light scattering (DLS) experiment and fluorescence lifetime measurement, we propose that the emergence of new emission band in the presence of HP2O73- ion is due to the aggregation-induced excimer formation.

Predicting Transcription Factor Binding Sites with Deep Learning.

Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with different approaches, Machine Learning being one of the most successful. Nevertheless, we note that many such approaches fail to propose a robust and meaningful method to embed the genetic data under analysis. We try to overcome this problem by proposing a bidirectional transformer-based encoder, empowered by bidirectional long-short term memory layers and with a capsule layer responsible for the final prediction. To evaluate the efficiency of the proposed approach, we use benchmark ChIP-seq datasets of five cell lines available in the ENCODE repository (A549, GM12878, Hep-G2, H1-hESC, and Hela). The results show that the proposed method can predict TFBS within the five different cell lines very well; moreover, cross-cell predictions provide satisfactory results as well. Experiments conducted across cell lines are reinforced by the analysis of five additional lines used only to test the model trained using the others. The results confirm that prediction across cell lines remains very high, allowing an extensive cross-transcription factor analysis to be performed from which several indications of interest for molecular biology may be drawn.

Whole genome analysis of more than 10 000 SARS-CoV-2 virus unveils global genetic diversity and target region of NSP6.

Whole genome analysis of SARS-CoV-2 is important to identify its genetic diversity. Moreover, accurate detection of SARS-CoV-2 is required for its correct diagnosis. To address these, first we have analysed publicly available 10 664 complete or near-complete SARS-CoV-2 genomes of 73 countries globally to find mutation points in the coding regions as substitution, deletion, insertion and single nucleotide polymorphism (SNP) globally and country wise. In this regard, multiple sequence alignment is performed in the presence of reference sequence from NCBI. Once the alignment is done, a consensus sequence is build to analyse each genomic sequence to identify the unique mutation points as substitutions, deletions, insertions and SNPs globally, thereby resulting in 7209, 11700, 119 and 53 such mutation points respectively. Second, in such categories, unique mutations for individual countries are determined with respect to other 72 countries. In case of India, unique 385, 867, 1 and 11 substitutions, deletions, insertions and SNPs are present in 566 SARS-CoV-2 genomes while 458, 1343, 8 and 52 mutation points in such categories are common with other countries. In majority (above 10%) of virus population, the most frequent and common mutation points between global excluding India and India are L37F, P323L, F506L, S507G, D614G and Q57H in NSP6, RdRp, Exon, Spike and ORF3a respectively. While for India, the other most frequent mutation points are T1198K, A97V, T315N and P13L in NSP3, RdRp, Spike and ORF8 respectively. These mutations are further visualised in protein structures and phylogenetic analysis has been done to show the diversity in virus genomes. Third, a web application is provided for searching mutation points globally and country wise. Finally, we have identified the potential conserved region as target that belongs to the coding region of ORF1ab, specifically to the NSP6 gene. Subsequently, we have provided the primers and probes using that conserved region so that it can be used for detecting SARS-CoV-2. Contact:indrajit@nitttrkol.ac.inSupplementary information: Supplementary data are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-Mutation-10K.

Characterisation of SARS-CoV-2 clades based on signature SNPs unveils continuous evolution.

Since the emergence of SARS-CoV-2 in Wuhan, China more than a year ago, it has spread across the world in a very short span of time. Although, different forms of vaccines are being rolled out for vaccination programs around the globe, the mutation of the virus is still a cause of concern among the research communities. Hence, it is important to study the constantly evolving virus and its strains in order to provide a much more stable form of cure. This fact motivated us to conduct this research where we have initially carried out multiple sequence alignment of 15359 and 3033 global dataset without Indian and the dataset of exclusive Indian SARS-CoV-2 genomes respectively, using MAFFT. Subsequently, phylogenetic analyses are performed using Nextstrain to identify virus clades. Consequently, the virus strains are found to be distributed among 5 major clades or clusters viz. 19A, 19B, 20A, 20B and 20C. Thereafter, mutation points as SNPs are identified in each clade. Henceforth, from each clade top 10 signature SNPs are identified based on their frequency i.e. number of occurrences in the virus genome. As a result, 50 such signature SNPs are individually identified for global dataset without Indian and dataset of exclusive Indian SARS-CoV-2 genomes respectively. Out of each 50 signature SNPs, 39 and 41 unique SNPs are identified among which 25 non-synonymous signature SNPs (out of 39) resulted in 30 amino acid changes in protein while 27 changes in amino acid are identified from 22 non-synonymous signature SNPs (out of 41). These 30 and 27 amino acid changes for the non-synonymous signature SNPs are visualised in their respective protein structure as well. Finally, in order to judge the characteristics of the identified clades, the non-synonymous signature SNPs are considered to evaluate the changes in proteins as biological functions with the sequences using PROVEAN and PolyPhen-2 while I-Mutant 2.0 is used to evaluate their structural stability. As a consequence, for global dataset without Indian sequences, G251V in ORF3a in clade 19A, F308Y and G196V in NSP4 and ORF3a in 19B are the unique amino acid changes which are responsible for defining each clade as they are all deleterious and unstable. Such changes which are common for both global dataset without Indian and dataset of exclusive Indian sequences are R203M in Nucleocapsid for 20B, T85I and Q57H in NSP2 and ORF3a respectively for 20C while for exclusive Indian sequences such unique changes are A97V in RdRp, G339S and G339C in NSP2 in 19A and Q57H in ORF3a in 20A.

Selective Metal-Ligand Bond-Breaking Driven by Weak Intermolecular Interactions: From Metamagnetic Mn(III)-Monomer to Hexacyanoferrate(II)-Bridged Metamagnetic Mn2Fe Trimer.

Metal-ligand coordination interactions are usually much stronger than weak intermolecular interactions. Nevertheless, here, we show experimental evidence and theoretical confirmation of a very rare example where metal-ligand bonds dissociate in an irreversible way, helped by a large number of weak intermolecular interactions that surpass the energy of the metal-ligand bond. Thus, we describe the design and synthesis of trinuclear Mn2Fe complex {[Mn(L)(H2O)]2Fe(CN)6},2- starting from a mononuclear Mn(III)-Schiff base complex: [Mn(L)(H2O)Cl] (1) and [Fe(CN)6]4- anions. This reaction implies the dissociation of Mn(III)-Cl coordination bonds and the formation of Mn(III)-NC bonds with the help of several intermolecular interactions. Here, we present the synthesis, crystal structure, and magnetic characterization of the monomeric Mn(III) complex [Mn(L)(H2O)Cl] (1) and of compound (H3O)[Mn(L)(H2O)2]{[Mn(L)(H2O)]2Fe(CN)6}·4H2O (2) (H2L = 2,2'-((1E,1'E)-(ethane-1,2-diylbis(azaneylylidene))bis(methaneylylidene))bis(4-methoxyphenol)). Complex 1 is a monomer where the Schiff base ligand (L) is coordinated to the four equatorial positions of the Mn(III) center with a H2O molecule and a Cl- ion at the axial sites and the monomeric units are assembled by π-π and hydrogen-bonding interactions to build supramolecular dimers. The combination of [Fe(CN)6]4- with complex 1 leads to the formation of linear Mn-NC-Fe-CN-Mn trimers where two trans cyano groups of the [Fe(CN)6]4- anion replace the labile chloride from the coordination sphere of two [Mn(L)(H2O)Cl] complexes, giving rise to the linear anionic {[Mn(L)(H2O)]2Fe(CN)6}2- trimer. This Mn2Fe trimer crystallizes with an oxonium cation and a mononuclear [Mn(L)(H2O)2]+ cation, closely related to the precursor neutral complex [Mn(L)(H2O)Cl]. In compound 2, the Mn2Fe trimers are assembled by several hydrogen-bonding and π-π interactions to frame an extended structure similar to that of complex 1. Density functional theoretical (DFT) calculations at the PBE1PBE-D3/def2-TZVP level show that the bond dissociation energy (-29.3 kcal/mol) for the Mn(III)-Cl bond is smaller than the summation of all the weak intermolecular interactions (-30.1 kcal/mol). Variable-temperature magnetic studies imply the existence of weak intermolecular antiferromagnetic couplings in both compounds, which can be can cancelled with a critical field of ca. 2.0 and 2.5 T at 2 K for compounds 1 and 2, respectively. The magnetic properties of compound 1 have been fit with a simple S = 2 monomer with g = 1.959, a weak zero-field splitting (|D| = 1.23 cm-1), and a very weak intermolecular interaction (zJ = -0.03 cm-1). For compound 2, we have used a model with an S = 2 monomer with ZFS plus an S = 2 antiferromagnetically coupled dimer with g = 2.009, |D| = 1.21 cm-1, and J = -0.42 cm-1. The metamagnetic behavior of both compounds is attributed to the weak intermolecular π-π and hydrogen-bonding interactions.

GhoMR: Multi-Receptive Lightweight Residual Modules for Hyperspectral Classification.

In recent years, hyperspectral images (HSIs) have attained considerable attention in computer vision (CV) due to their wide utility in remote sensing. Unlike images with three or lesser channels, HSIs have a large number of spectral bands. Recent works demonstrate the use of modern deep learning based CV techniques like convolutional neural networks (CNNs) for analyzing HSI. CNNs have receptive fields (RFs) fueled by learnable weights, which are trained to extract useful features from images. In this work, a novel multi-receptive CNN module called GhoMR is proposed for HSI classification. GhoMR utilizes blocks containing several RFs, extracting features in a residual fashion. Each RF extracts features which are used by other RFs to extract more complex features in a hierarchical manner. However, the higher the number of RFs, the greater the associated weights, thus heavier is the network. Most complex architectures suffer from this shortcoming. To tackle this, the recently found Ghost module is used as the basic building unit. Ghost modules address the feature redundancy in CNNs by extracting only limited features and performing cheap transformations on them, thus reducing the overall parameters in the network. To test the discriminative potential of GhoMR, a simple network called GhoMR-Net is constructed using GhoMR modules, and experiments are performed on three public HSI data sets-Indian Pines, University of Pavia, and Salinas Scene. The classification performance is measured using three metrics-overall accuracy (OA), Kappa coefficient (Kappa), and average accuracy (AA). Comparisons with ten state-of-the-art architectures are shown to demonstrate the effectiveness of the method further. Although lightweight, the proposed GhoMR-Net provides comparable or better performance than other networks. The PyTorch code for this study is made available at the iamarijit/GhoMR GitHub repository.

Genome-wide analysis of multi-view data of miRNA-seq to identify miRNA biomarkers for stomach cancer.

Stomach cancer is one of the leading causes of cancer-related deaths worldwide. More than 80% diagnosis of this cancer occur at later stages leading to low 5-year survival rate. This emphasizes the need to have better prognostic techniques for stomach cancer. In this regard, the Next-Generation Sequencing of whole genome and multi-view approach to omics may reveal the underlying molecular complexity of stomach cancer using high throughput expression data of miRNA. Generally, miRNAs are small, non-coding RNAs, which cause downregulation of target mRNAs. They also show differential expression for a specific biological condition like stage or histological type of stomach cancer, highlighting their importance as potential biomarkers. Analyzing miRNA expression data is a challenging task due to the existence of large number of miRNAs and less sample size. A small set of miRNAs will be helpful in designing efficient diagnostic and prognostic tool. In this regard, here a computational framework is proposed that selects different sets of miRNAs for five different categories of clinical outcomes viz. condition, clinical stage, age, histological type, and survival status. First, the miRNAs are ranked using four feature ranking methods. These ranks are used to find an ensemble rank based on adaptive weight. Second, the top 100 miRNAs from each category are used to find the miRNAs that are common to all categories as well as miRNAs that belong to only one category. Finally, the results have been validated quantitatively and through biological significance analysis.

Evaluation of Gall Bladder Stones Using Susceptibility Weighted Imaging.

OBJECTIVE: To evaluate the visualization of gallbladder stones on susceptibility-weighted imaging (SWI). MATERIALS AND METHODS: Imaging data from 47 patients who underwent clinically indicated cholecystectomy was reviewed. Breath-hold SWI was added to the magnetic resonance imaging protocol and magnitude and phase data was reviewed for gall-stones visualization. Phase signature, that is, diamagnetic, paramagnetic, or mixed, was also noted in the stones. Magnetic susceptibility value of surgically extracted gallstones were imaged ex vivo (n = 37). RESULTS: In 45 of 47 cases, gallstones were surgically confirmed. In 43 cases, gallstones were visualized in the SWI. In 1 case, although routine imaging failed, stones were visualized on SWI. In 29 diamagnetic, 7 paramagnetic and 9 cases mixed phase were seen. In an ex vivo study, magnetic susceptibility of stones was found ranging between -0.102 and -0.916 ppm for diamagnetic and 0.203 and 486 ppm for paramagnetic stones. CONCLUSIONS: Gallbladder stones can be visualized with SWI and may be added to the routine magnetic resonance imaging protocol for its evaluation.

Are household food security, nutrient adequacy, and childhood nutrition clustered together? A cross-sectional study in Bankura, West Bengal.

BACKGROUND: Research on different measures of food security and their interrelation in order to identify vulnerable households are scarce in India. OBJECTIVES: The objective was to assess household food security (HHFS), nutrient adequacy, dietary diversity, and nutritional status of under-five children along with their interrelation in the slums of Bankura Municipality, West Bengal. METHODS: A cross-sectional study was conducted during 2016-2017 among 240 households using two-stage 30-cluster random sampling. Information regarding socioeconomic characteristics, availability, and utilization of different poverty alleviation schemes was collected. HHFS was assessed by a validated HHFS scale-short form in Bengali and nutrient adequacy with 24-h recall method. The eldest under-five child in the family was measured for anthropometry using standard procedure and for dietary diversity with the Individual Dietary Diversity Score. RESULTS: Overall, 74 (29.1%) households had "food security," whereas 102 (44.3%) and 64 (26.6%) had, respectively, low and very low food security. Among 190 under-five children, 63 (35.3%) had single and 50 (25.5%) had multiple anthropometric failures. Overall, 89 (36.1%) households were deficient for both energy and protein and 111 (47.6%) had deficiency of either of these two. Indicators on the utilization of different poverty alleviation schemes were associated with low/very low food security. A "Composite Index of Food Scarcity" comprising of HHFS, nutrient adequacy, and dietary diversity was proposed which was found to have dose-response relationship with grades of anthropometric failure of under-five children. CONCLUSIONS: An index comprising of three indicators might help identify the vulnerable households in relation to food security more effectively than a single indicator.

Advanced and amplified BOLD fluctuations in high-grade gliomas.

BACKGROUND: Glioma grade along with patient's age and general health are used for treatment planning and prognosis. PURPOSE: To characterize and quantify the spontaneous blood oxygen level-dependent (BOLD) fluctuations in gliomas using measures based on T2*-weighted signal time-series and to distinguish between high- and low-grade gliomas. STUDY TYPE: Retrospective. SUBJECTS: Twenty-one patients with high-grade and 13 patients with low-grade gliomas confirmed on histology were investigated. FIELD STRENGTH/SEQUENCE: Dynamic T2*-weighted (multislice single-shot echo-planar-imaging) magnetic resonance imaging (MRI) was performed on a 3T system with an 8-element receive-only head coil to measure the BOLD fluctuations. In addition, a dynamic T1 -weighted (3D fast field echo) dynamic contrast-enhanced (DCE) perfusion scan was performed. ASSESSMENT: Three BOLD measures were determined: the temporal shift (TS), amplitude of low frequency fluctuations (ALFF), and regional homogeneity (ReHo). DCE perfusion-based cerebral blood volume (CBV) and time-to-peak (TTP) maps were concurrently evaluated for comparison. STATISTICAL TESTS: An analysis-of-variance test was first used. When the test appeared significant, post-hoc analysis was performed using analysis-of-covariance with age as covariate. Logistic regression and receiver-operator characteristic curve analysis were also performed. RESULTS: TS was significantly advanced in high-grade gliomas compared to the contralateral cortex (P = 0.01) and low-grade gliomas (P = 0.009). In high-grade gliomas, ALFF and CBV were significantly higher than the contralateral cortex (P = 0.041 and P = 0.008, respectively) and low-grade gliomas (P = 0.036 and P = 0.01, respectively). ReHo and TTP did not show significant differences between high- and low-grade gliomas (P = 0.46 and P = 0.42, respectively). The area-under-curve was above 0.7 only for the TS, ALFF, and CBV measures. DATA CONCLUSION: Advanced and amplified hemodynamic fluctuations manifest in high-grade gliomas, but not in low-grade gliomas, and can be assessed using BOLD measures. Preliminary results showed that quantification of spontaneous fluctuations has potential for hemodynamic characterization of gliomas and distinguishing between high- and low-grade gliomas. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:1616-1625.

Susceptibility Weighted With Quantitative Phase Magnetic Resonance Imaging in Differentiation of Various Stages of Hemorrhage and Calcification in Female Pelvic Pathologies: A Preliminary Study.

BACKGROUND: The purposes of this study were to assess the value of phase for characterization of female pelvic lesions with hemorrhage in various stages and to differentiate them from calcified lesions at 3.0-T magnetic resonance imaging (MRI). METHODS: Forty-four female patients with hemorrhagic (n = 37) or calcified (n = 7) pelvic pathology underwent conventional MRI including susceptibility-weighted imaging with phase information. Hemorrhagic lesions were grouped into acute, subacute, and chronic, and calcified lesions were detected on the basis of conventional imaging findings. Phase quantification of these hemorrhagic and calcified lesions was performed. RESULTS: The phase values significantly differed (P < 0.001) among various stages of hemorrhage, as well as calcification (chronic hemorrhage, -65.09 ± 9.09 degrees; subacute hemorrhage, -11.41 ± 4.4 degrees; acute hemorrhage, -42.30 ± 5.20 degrees; and calcified lesions, 117.55 ± 12.93 degrees). CONCLUSIONS: Quantitative phase imaging has the potential to differentiate various stages of hemorrhagic and calcified pathologies. This may add value to the conventional MRI in improved characterization of these entities in female pelvic pathologies.

Binding Activity Prediction of Cyclin-Dependent Inhibitors.

The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust estimation of ligand binding activity has been a challenging task in drug design. In this regard, various machine learning techniques, such as Support Vector Machine, Naive Bayesian classifier, Decision Tree, and K-Nearest Neighbor classifier, have been used. The performance of these heterogeneous classification techniques depends on proper selection of features from the data set. This fact motivated us to propose an integrated classification technique using Genetic Algorithm (GA), Rotational Feature Selection (RFS) scheme, and Ensemble of Machine Learning methods, named as the Genetic Algorithm integrated Rotational Ensemble based classification technique, for the prediction of ligand binding activity of CDKs. This technique can automatically find the important features and the ensemble size. For this purpose, GA encodes the features and ensemble size in a chromosome as a binary string. Such encoded features are then used to create diverse sets of training points using RFS in order to train the machine learning method multiple times. The RFS scheme works on Principal Component Analysis (PCA) to preserve the variability information of the rotational nonoverlapping subsets of original data. Thereafter, the testing points are fed to the different instances of trained machine learning method in order to produce the ensemble result. Here accuracy is computed as a final result after 10-fold cross validation, which also used as an objective function for GA to maximize. The effectiveness of the proposed classification technique has been demonstrated quantitatively and visually in comparison with different machine learning methods for 16 ligand binding CDK docking and rescoring data sets. In addition, the best possible features have been reported for CDK docking and rescoring data sets separately. Finally, the Friedman test has been conducted to judge the statistical significance of the results produced by the proposed technique. The results indicate that the integrated classification technique has high relevance in predicting of protein-ligand binding activity.

Consensus classification of human leukocyte antigen class II proteins.

Class II human leukocyte antigens (HLA II) are proteins involved in the human immunological adaptive response by binding and exposing some pre-processed, non-self peptides in the extracellular domain in order to make them recognizable by the CD4+ T lymphocytes. However, the understanding of HLA-peptide binding interaction is a crucial step for designing a peptide-based vaccine because the high rate of polymorphisms in HLA class II molecules creates a big challenge, even though the HLA II proteins can be grouped into supertypes, where members of different class bind a similar pool of peptides. Hence, first we performed the supertype classification of 27 HLA II proteins using their binding affinities and structural-based linear motifs to create a stable group of supertypes. For this purpose, a well-known clustering method was used, and then, a consensus was built to find the stable groups and to show the functional and structural correlation of HLA II proteins. Thus, the overlap of the binding events was measured, confirming a large promiscuity within the HLA II-peptide interactions. Moreover, a very low rate of locus-specific binding events was observed for the HLA-DP genetic locus, suggesting a different binding selectivity of these proteins with respect to HLA-DR and HLA-DQ proteins. Secondly, a predictor based on a support vector machine (SVM) classifier was designed to recognize HLA II-binding peptides. The efficiency of prediction was estimated using precision, recall (sensitivity), specificity, accuracy, F-measure, and area under the ROC curve values of random subsampled dataset in comparison with other supervised classifiers. Also the leave-one-out cross-validation was performed to establish the efficiency of the predictor. The availability of HLA II-peptide interaction dataset, HLA II-binding motifs, high-quality amino acid indices, peptide dataset for SVM training, and MATLAB code of the predictor is available at http://sysbio.icm.edu.pl/HLA .

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19.

COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human-spike protein-protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug-protein interactions can be predicted for human-SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug-protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human-spike PPIs and eventually new drug-protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human-spike PPI and 96.15% for drug-protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.

Transcription Factor Driven Gene Regulation in COVID-19 Patients.

SARS-CoV-2 and its many variants have caused a worldwide emergency. Host cells colonised by SARS-CoV-2 present a significantly different gene expression landscape. As expected, this is particularly true for genes that directly interact with virus proteins. Thus, understanding the role that transcription factors can play in driving differential regulation in patients affected by COVID-19 is a focal point to unveil virus infection. In this regard, we have identified 19 transcription factors which are predicted to target human proteins interacting with Spike glycoprotein of SARS-CoV-2. Transcriptomics RNA-Seq data derived from 13 human organs are used to analyse expression correlation between identified transcription factors and related target genes in both COVID-19 patients and healthy individuals. This resulted in the identification of transcription factors showing the most relevant impact in terms of most evident differential correlation between COVID-19 patients and healthy individuals. This analysis has also identified five organs such as the blood, heart, lung, nasopharynx and respiratory tract in which a major effect of differential regulation mediated by transcription factors is observed. These organs are also known to be affected by COVID-19, thereby providing consistency to our analysis. Furthermore, 31 key human genes differentially regulated by the transcription factors in the five organs are identified and the corresponding KEGG pathways and GO enrichment are also reported. Finally, the drugs targeting those 31 genes are also put forth. This in silico study explores the effects of transcription factors on human genes interacting with Spike glycoprotein of SARS-CoV-2 and intends to provide new insights to inhibit the virus infection.

Transmembrane fluoride anion transport by meso-3,5-bis(trifluoromethyl)phenyl picket calix[4]pyrrole.

Meso-3,5-bis(trifluoromethyl)phenyl picket calix[4]pyrrole 1 displays excellent fluoride anion transport activity across artificial lipid bilayers showing EC50 = 2.15 µM (at 450 s in EYPC vesicle) with high fluoride over chloride ion selectivity. The high fluoride selectivity of 1 was ascribed to the formation of a sandwich type π-anion-π interaction complex.

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.